R Notebook - EDA workflow template using Nelson data
Rich Hartman, PhD
December 18th, 2019
install required packages (only need to do this once?)
install.packages(“ggplot2”); # for graphics functions install.packages(“car”); # for the leveneTest() function install.packages(“pastecs”); # for the stat.desc() function install.packages(“psych”); # for the describe() function install.packages(“hrbrthemes”) install.packages(“viridis”)
“call” the required packages (need to do this every session?)
library(car); library(ggplot2); library(pastecs); library(psych); library(hrbrthemes); library(viridis)Loading required package: carData
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Attaching package: ‘psych’
The following objects are masked from ‘package:ggplot2’:
%+%, alpha
The following object is masked from ‘package:car’:
logit
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
Loading required package: viridisLiteUse Excel to generate a .csv file with “tidy” data (each row = 1 case / subject, 1st row is column names). Import .CSV file into R “dataframe” called “watermazedata”. Then show the “watermazedata” dataframe (header + 1st 8 data rows) to check it out
watermazedata <- read.csv(file="./data_clean/water maze all.csv", header=TRUE, sep=",")
watermazedataDerive new variables. Mostly use the rowMeans() function, but these may be useful as well…
Make some new DVs (~“columns”) assigned 1 (TRUE) or 0 (FALSE) based on Boolean calculations: - Less than? watermazedata\(Duration.Spatial2LessThanSpatial1 <- watermazedata\)Duration.Spatial2 < watermazedata\(Duration.Spatial2 - Less than or equal to? watermazedata\)Duration.Spatial1LessThanOrEqualTo60 <- watermazedata\(Duration.Spatial2 <= 60 - Equal to? watermazedata\)Sh <- watermazedata\(Treatment == "Sh" - Not equal to? watermazedata\)NotSh <- watermazedata$Treatment != “Sh”
Use “ifelse” to maybe replace scores (e.g., replace any duration greater than 60 with NA “missing data”, else keep the same) - watermazedata\(Duration.Spatial.Clean <- ifelse(watermazedata\)Duration.Spatial > 60, NA, watermazedata$Duration.Spatial)
# Trials 1-10 for cued, spatial 1, spatial 2 and spatial trials averaged into 5 blocks (2 trials each) each for both Distance and Duration
watermazedata$Duration.Cued.Block1 <- rowMeans(cbind
(watermazedata$Duration.Cued.1,
watermazedata$Duration.Cued.2),
na.rm = TRUE)
watermazedata$Duration.Cued.Block2 <- rowMeans(cbind
(watermazedata$Duration.Cued.3,
watermazedata$Duration.Cued.4),
na.rm = TRUE)
watermazedata$Duration.Cued.Block3 <- rowMeans(cbind
(watermazedata$Duration.Cued.5,
watermazedata$Duration.Cued.6),
na.rm = TRUE)
watermazedata$Duration.Cued.Block4 <- rowMeans(cbind
(watermazedata$Duration.Cued.7,
watermazedata$Duration.Cued.8),
na.rm = TRUE)
watermazedata$Duration.Cued.Block5 <- rowMeans(cbind
(watermazedata$Duration.Cued.9,
watermazedata$Duration.Cued.10),
na.rm = TRUE)
watermazedata$Duration.Spatial1.Block1 <- rowMeans(cbind
(watermazedata$Duration.Spatial1.1,
watermazedata$Duration.Spatial1.2),
na.rm = TRUE)
watermazedata$Duration.Spatial1.Block2 <- rowMeans(cbind
(watermazedata$Duration.Spatial1.3,
watermazedata$Duration.Spatial1.4),
na.rm = TRUE)
watermazedata$Duration.Spatial1.Block3 <- rowMeans(cbind
(watermazedata$Duration.Spatial1.5,
watermazedata$Duration.Spatial1.6),
na.rm = TRUE)
watermazedata$Duration.Spatial1.Block4 <- rowMeans(cbind
(watermazedata$Duration.Spatial1.7,
watermazedata$Duration.Spatial1.8),
na.rm = TRUE)
watermazedata$Duration.Spatial1.Block5 <- rowMeans(cbind
(watermazedata$Duration.Spatial1.9,
watermazedata$Duration.Spatial1.10),
na.rm = TRUE)
watermazedata$Duration.Spatial2.Block1 <- rowMeans(cbind
(watermazedata$Duration.Spatial2.1,
watermazedata$Duration.Spatial2.2),
na.rm = TRUE)
watermazedata$Duration.Spatial2.Block2 <- rowMeans(cbind
(watermazedata$Duration.Spatial2.3,
watermazedata$Duration.Spatial2.4),
na.rm = TRUE)
watermazedata$Duration.Spatial2.Block3 <- rowMeans(cbind
(watermazedata$Duration.Spatial2.5,
watermazedata$Duration.Spatial2.6),
na.rm = TRUE)
watermazedata$Duration.Spatial2.Block4 <- rowMeans(cbind
(watermazedata$Duration.Spatial2.7,
watermazedata$Duration.Spatial2.8),
na.rm = TRUE)
watermazedata$Duration.Spatial2.Block5 <- rowMeans(cbind
(watermazedata$Duration.Spatial2.9,
watermazedata$Duration.Spatial2.10),
na.rm = TRUE)
watermazedata$Duration.Spatial3.Block1 <- rowMeans(cbind
(watermazedata$Duration.Spatial3.1,
watermazedata$Duration.Spatial3.2),
na.rm = TRUE)
watermazedata$Duration.Spatial3.Block2 <- rowMeans(cbind
(watermazedata$Duration.Spatial3.3,
watermazedata$Duration.Spatial3.4),
na.rm = TRUE)
watermazedata$Duration.Spatial3.Block3 <- rowMeans(cbind
(watermazedata$Duration.Spatial3.5,
watermazedata$Duration.Spatial3.6),
na.rm = TRUE)
watermazedata$Duration.Spatial3.Block4 <- rowMeans(cbind
(watermazedata$Duration.Spatial3.7,
watermazedata$Duration.Spatial3.8),
na.rm = TRUE)
watermazedata$Duration.Spatial3.Block5 <- rowMeans(cbind
(watermazedata$Duration.Spatial3.9,
watermazedata$Duration.Spatial3.10),
na.rm = TRUE)
watermazedata$Distance.Cued.Block1 <- rowMeans(cbind
(watermazedata$Distance.Cued.1,
watermazedata$Distance.Cued.2),
na.rm = TRUE)
watermazedata$Distance.Cued.Block2 <- rowMeans(cbind
(watermazedata$Distance.Cued.3,
watermazedata$Distance.Cued.4),
na.rm = TRUE)
watermazedata$Distance.Cued.Block3 <- rowMeans(cbind
(watermazedata$Distance.Cued.5,
watermazedata$Distance.Cued.6),
na.rm = TRUE)
watermazedata$Distance.Cued.Block4 <- rowMeans(cbind
(watermazedata$Distance.Cued.7,
watermazedata$Distance.Cued.8),
na.rm = TRUE)
watermazedata$Distance.Cued.Block5 <- rowMeans(cbind
(watermazedata$Distance.Cued.9,
watermazedata$Distance.Cued.10),
na.rm = TRUE)
watermazedata$Distance.Spatial1.Block1 <- rowMeans(cbind
(watermazedata$Distance.Spatial1.1,
watermazedata$Distance.Spatial1.2),
na.rm = TRUE)
watermazedata$Distance.Spatial1.Block2 <- rowMeans(cbind
(watermazedata$Distance.Spatial1.3,
watermazedata$Distance.Spatial1.4),
na.rm = TRUE)
watermazedata$Distance.Spatial1.Block3 <- rowMeans(cbind
(watermazedata$Distance.Spatial1.5,
watermazedata$Distance.Spatial1.6),
na.rm = TRUE)
watermazedata$Distance.Spatial1.Block4 <- rowMeans(cbind
(watermazedata$Distance.Spatial1.7,
watermazedata$Distance.Spatial1.8),
na.rm = TRUE)
watermazedata$Distance.Spatial1.Block5 <- rowMeans(cbind
(watermazedata$Distance.Spatial1.9,
watermazedata$Distance.Spatial1.10),
na.rm = TRUE)
watermazedata$Distance.Spatial2.Block1 <- rowMeans(cbind
(watermazedata$Distance.Spatial2.1,
watermazedata$Distance.Spatial2.2),
na.rm = TRUE)
watermazedata$Distance.Spatial2.Block2 <- rowMeans(cbind
(watermazedata$Distance.Spatial2.3,
watermazedata$Distance.Spatial2.4),
na.rm = TRUE)
watermazedata$Distance.Spatial2.Block3 <- rowMeans(cbind
(watermazedata$Distance.Spatial2.5,
watermazedata$Distance.Spatial2.6),
na.rm = TRUE)
watermazedata$Distance.Spatial2.Block4 <- rowMeans(cbind
(watermazedata$Distance.Spatial2.7,
watermazedata$Distance.Spatial2.8),
na.rm = TRUE)
watermazedata$Distance.Spatial2.Block5 <- rowMeans(cbind
(watermazedata$Distance.Spatial2.9,
watermazedata$Distance.Spatial2.10),
na.rm = TRUE)
watermazedata$Distance.Spatial3.Block1 <- rowMeans(cbind
(watermazedata$Distance.Spatial3.1,
watermazedata$Distance.Spatial3.2),
na.rm = TRUE)
watermazedata$Distance.Spatial3.Block2 <- rowMeans(cbind
(watermazedata$Distance.Spatial3.3,
watermazedata$Distance.Spatial3.4),
na.rm = TRUE)
watermazedata$Distance.Spatial3.Block3 <- rowMeans(cbind
(watermazedata$Distance.Spatial3.5,
watermazedata$Distance.Spatial3.6),
na.rm = TRUE)
watermazedata$Distance.Spatial3.Block4 <- rowMeans(cbind
(watermazedata$Distance.Spatial3.7,
watermazedata$Distance.Spatial3.8),
na.rm = TRUE)
watermazedata$Distance.Spatial3.Block5 <- rowMeans(cbind
(watermazedata$Distance.Spatial3.9,
watermazedata$Distance.Spatial3.10),
na.rm = TRUE)
# Cued, spatial 1, spatial 2 and spatial blocks 1-5 averaged into Overall Averages for both Distance and Duration
watermazedata$Duration.Cued <- rowMeans(cbind (watermazedata$Duration.Cued.1,
watermazedata$Duration.Cued.2,
watermazedata$Duration.Cued.3,
watermazedata$Duration.Cued.4,
watermazedata$Duration.Cued.5),
na.rm = TRUE)
watermazedata$Duration.Spatial1 <- rowMeans(cbind (watermazedata$Duration.Spatial1.1,
watermazedata$Duration.Spatial1.2,
watermazedata$Duration.Spatial1.3,
watermazedata$Duration.Spatial1.4,
watermazedata$Duration.Spatial1.5),
na.rm = TRUE)
watermazedata$Duration.Spatial2 <- rowMeans(cbind (watermazedata$Duration.Spatial2.1,
watermazedata$Duration.Spatial2.2,
watermazedata$Duration.Spatial2.3,
watermazedata$Duration.Spatial2.4,
watermazedata$Duration.Spatial2.5),
na.rm = TRUE)
watermazedata$Duration.Spatial3 <- rowMeans(cbind (watermazedata$Duration.Spatial3.1,
watermazedata$Duration.Spatial3.2,
watermazedata$Duration.Spatial3.3,
watermazedata$Duration.Spatial3.4,
watermazedata$Duration.Spatial3.5),
na.rm = TRUE)
watermazedata$Duration.Spatial <- rowMeans(cbind (watermazedata$Duration.Spatial1,
watermazedata$Duration.Spatial2,
watermazedata$Duration.Spatial3),
na.rm = TRUE)
watermazedata$Distance.Cued <- rowMeans(cbind (watermazedata$Distance.Cued.1,
watermazedata$Distance.Cued.2,
watermazedata$Distance.Cued.3,
watermazedata$Distance.Cued.4,
watermazedata$Distance.Cued.5),
na.rm = TRUE)
watermazedata$Distance.Spatial1 <- rowMeans(cbind (watermazedata$Distance.Spatial1.1,
watermazedata$Distance.Spatial1.2,
watermazedata$Distance.Spatial1.3,
watermazedata$Distance.Spatial1.4,
watermazedata$Distance.Spatial1.5),
na.rm = TRUE)
watermazedata$Distance.Spatial2 <- rowMeans(cbind (watermazedata$Distance.Spatial2.1,
watermazedata$Distance.Spatial2.2,
watermazedata$Distance.Spatial2.3,
watermazedata$Distance.Spatial2.4,
watermazedata$Distance.Spatial2.5),
na.rm = TRUE)
watermazedata$Distance.Spatial3 <- rowMeans(cbind (watermazedata$Distance.Spatial3.1,
watermazedata$Distance.Spatial3.2,
watermazedata$Distance.Spatial3.3,
watermazedata$Distance.Spatial3.4,
watermazedata$Distance.Spatial3.5),
na.rm = TRUE)
watermazedata$Distance.Spatial <- rowMeans(cbind (watermazedata$Distance.Spatial1,
watermazedata$Distance.Spatial2,
watermazedata$Distance.Spatial3),
na.rm = TRUE)
# Make a Speed variable (Distance/Duration)
watermazedata$Speed <- watermazedata$Distance.Spatial / watermazedata$Duration.Spatial
# Make working memory variables
watermazedata$Working.Duration.Trial1.1 <- watermazedata$Duration.Spatial1.1
watermazedata$Working.Duration.Trial2.1 <- watermazedata$Duration.Spatial1.2
watermazedata$Working.Duration.Diff.1 <- watermazedata$Duration.Spatial1.1 - watermazedata$Duration.Spatial1.2
watermazedata$Working.Duration.Trial1.2 <- watermazedata$Duration.Spatial2.1
watermazedata$Working.Duration.Trial2.2 <- watermazedata$Duration.Spatial2.2
watermazedata$Working.Duration.Diff.2 <- watermazedata$Duration.Spatial2.1 - watermazedata$Duration.Spatial2.2
watermazedata$Working.Duration.Trial1.3 <- watermazedata$Duration.Spatial3.1
watermazedata$Working.Duration.Trial2.3 <- watermazedata$Duration.Spatial3.2
watermazedata$Working.Duration.Diff.3 <- watermazedata$Duration.Spatial3.1 - watermazedata$Duration.Spatial3.2
watermazedata$Working.Duration.Trial1.Ave <- (watermazedata$Duration.Spatial1.1 + watermazedata$Duration.Spatial2.1 + watermazedata$Duration.Spatial3.1) / 3
watermazedata$Working.Duration.Trial2.Ave <- (watermazedata$Duration.Spatial1.2 + watermazedata$Duration.Spatial2.2 + watermazedata$Duration.Spatial3.2) / 3
watermazedata$Working.Duration.Diff.Ave <- (watermazedata$Working.Duration.Diff.1 + watermazedata$Working.Duration.Diff.2 + watermazedata$Working.Duration.Diff.3) / 3
watermazedata$Working.Distance.Trial1.1 <- watermazedata$Distance.Spatial1.1
watermazedata$Working.Distance.Trial2.1 <- watermazedata$Distance.Spatial1.2
watermazedata$Working.Distance.Diff.1 <- watermazedata$Distance.Spatial1.1 - watermazedata$Distance.Spatial1.2
watermazedata$Working.Distance.Trial1.2 <- watermazedata$Distance.Spatial2.1
watermazedata$Working.Distance.Trial2.2 <- watermazedata$Distance.Spatial2.2
watermazedata$Working.Distance.Diff.2 <- watermazedata$Distance.Spatial2.1 - watermazedata$Distance.Spatial2.2
watermazedata$Working.Distance.Trial1.3 <- watermazedata$Distance.Spatial3.1
watermazedata$Working.Distance.Trial2.3 <- watermazedata$Distance.Spatial3.2
watermazedata$Working.Distance.Diff.3 <- watermazedata$Distance.Spatial3.1 - watermazedata$Distance.Spatial3.2
watermazedata$Working.Distance.Trial1.Ave <- (watermazedata$Distance.Spatial1.1 + watermazedata$Distance.Spatial2.1 + watermazedata$Distance.Spatial3.1) / 3
watermazedata$Working.Distance.Trial2.Ave <- (watermazedata$Distance.Spatial1.2 + watermazedata$Distance.Spatial2.2 + watermazedata$Distance.Spatial3.2) / 3
watermazedata$Working.Distance.Diff.Ave <- (watermazedata$Working.Distance.Diff.1 + watermazedata$Working.Distance.Diff.2 + watermazedata$Working.Distance.Diff.3) / 3
Create a “subset” dataframe for each group to ease making histograms/normal curves and QQ plots by group.
Ac_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Ac")
Fx_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Fx")
Sh_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Sh")How many subjects are missing data from a specific column? (na = ‘missing’). Make a variable that returns 1 (TRUE) if data is missing: watermazedata\(Duration.Spatial1.Missing <- is.na(watermazedata\)Duration.Spatial1)
How many subjects are missing from Duration.Spatial1 data? sum(watermazedata$Duration.Spatial1.Missing)
1 = missing, 0 = there, so mean will tell us proportion of cases missing data in that variable
….or simply calculate this WITHOUT making a new variable:
sum(is.na(watermazedata$Duration.Cued)); mean(is.na(watermazedata$Duration.Cued))[1] 0
[1] 0sum(is.na(watermazedata$Duration.Spatial1)); mean(is.na(watermazedata$Duration.Spatial1))[1] 0
[1] 0sum(is.na(watermazedata$Duration.Spatial2)); mean(is.na(watermazedata$Duration.Spatial2))[1] 0
[1] 0sum(is.na(watermazedata$Duration.Spatial3)); mean(is.na(watermazedata$Duration.Spatial3))[1] 0
[1] 0sum(is.na(watermazedata$Duration.Spatial)); mean(is.na(watermazedata$Duration.Spatial))[1] 0
[1] 0sum(is.na(watermazedata$Distance.Cued)); mean(is.na(watermazedata$Distance.Cued))[1] 0
[1] 0sum(is.na(watermazedata$Distance.Spatial1)); mean(is.na(watermazedata$Distance.Spatial1))[1] 0
[1] 0sum(is.na(watermazedata$Distance.Spatial2)); mean(is.na(watermazedata$Distance.Spatial2))[1] 0
[1] 0sum(is.na(watermazedata$Distance.Spatial3)); mean(is.na(watermazedata$Distance.Spatial3))[1] 0
[1] 0sum(is.na(watermazedata$Distance.Spatial)); mean(is.na(watermazedata$Distance.Spatial))[1] 0
[1] 0sum(is.na(watermazedata$Speed)); mean(is.na(watermazedata$Speed))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Entries.1)); mean(is.na(watermazedata$Probe.Entries.1))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Entries.2)); mean(is.na(watermazedata$Probe.Entries.2))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Entries.3)); mean(is.na(watermazedata$Probe.Entries.3))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Entries.Ave)); mean(is.na(watermazedata$Probe.Entries.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Percent1)); mean(is.na(watermazedata$Probe.Percent1))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Percent2)); mean(is.na(watermazedata$Probe.Percent2))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Percent3)); mean(is.na(watermazedata$Probe.Percent3))[1] 0
[1] 0sum(is.na(watermazedata$Probe.Percent.Ave)); mean(is.na(watermazedata$Probe.Percent.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Probe2.Opposite.Percent)); mean(is.na(watermazedata$Probe2.Opposite.Percent))[1] 0
[1] 0sum(is.na(watermazedata$Working.Duration.Trial1.Ave)); mean(is.na(watermazedata$Working.Duration.Trial1.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Working.Duration.Trial2.Ave)); mean(is.na(watermazedata$Working.Duration.Trial2.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Working.Duration.Diff.Ave)); mean(is.na(watermazedata$Working.Duration.Diff.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Working.Distance.Trial1.Ave)); mean(is.na(watermazedata$Working.Distance.Trial1.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Working.Distance.Trial2.Ave)); mean(is.na(watermazedata$Working.Distance.Trial2.Ave))[1] 0
[1] 0sum(is.na(watermazedata$Working.Distance.Diff.Ave)); mean(is.na(watermazedata$Working.Distance.Diff.Ave))[1] 0
[1] 0Now, start checking the various aussumptions (normality, homogeneity of variance, etc.) for all meaningful DVS - e.g., Average Distance and Distance for days 1-3 are probably important, but Blocks 1-5 from each are probably not (?). If the idea is to compare groups, the assumptions need to be tested with each variable broken down by group.
??? is there a “Bonferroni correction” for multiple tests of Normality etc ???
Generate some descriptive stats for the variables of interest.
NOTE: For output reported using “e”: e+02, simply “move” the decimal point 2 places to right. e-02 = move decimal 2 places to left…
Can use describe() (from the psych package) or stat.desc() function (from the pastecs package) to get some basic stats.
# describe()
# Overall DVs (not broken down by group)
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = describe)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, describe)
# multiple variables at once:
# describe(cbind(watermazedata$Duration.Cued,
# watermazedata$Duration.Spatial1,
# watermazedata$Duration.Spatial2,
# watermazedata$Duration.Spatial3,
# watermazedata$Duration.Spatial,
# watermazedata$Distance.Cued,
# watermazedata$Distance.Spatial1,
# watermazedata$Distance.Spatial2,
# watermazedata$Distance.Spatial3,
# watermazedata$Distance.Spatial,
# watermazedata$Speed,
# watermazedata$Probe.Entries.1,
# watermazedata$Probe.Entries.2,
# watermazedata$Probe.Entries.3,
# watermazedata$Probe.Entries.Ave,
# watermazedata$Probe.Percent1,
# watermazedata$Probe.Percent2,
# watermazedata$Probe.Percent3,
# watermazedata$Probe.Percent.Ave,
# watermazedata$Probe2.Opposite.Percent,
# watermazedata$Working.Duration.Trial1.Ave,
# watermazedata$Working.Duration.Trial2.Ave,
# watermazedata$Working.Duration.Diff.Ave,
# watermazedata$Working.Distance.Trial1.Ave,
# watermazedata$Working.Distance.Trial2.Ave,
# watermazedata$Working.Distance.Diff.Ave))
# or
# describe(watermazedata[,c("Duration.Spatial1",
# "Duration.Spatial2",
# "Duration.Spatial3")]); # ETC
# broken down by group
#by(cbind(Duration.Cued=watermazedata$Duration.Cued,
# Duration.Spatial1=watermazedata$Duration.Spatial1,
# Duration.Spatial2=watermazedata$Duration.Spatial2,
# Duration.Spatial3=watermazedata$Duration.Spatial3,
# Duration.Spatial=watermazedata$Duration.Spatial,
# Distance.Cued=watermazedata$Distance.Cued,
# Distance.Spatial1=watermazedata$Distance.Spatial1,
# Distance.Spatial2=watermazedata$Distance.Spatial2,
# Distance.Spatial3=watermazedata$Distance.Spatial3,
# Distance.Spatial=watermazedata$Distance.Spatial,
# Speed=watermazedata$Speed,
# Probe.Entries.1=watermazedata$Probe.Entries.1,
# Probe.Entries.2=watermazedata$Probe.Entries.2,
# Probe.Entries.3=watermazedata$Probe.Entries.3,
# Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
# Probe.Percent1=watermazedata$Probe.Percent1,
# Probe.Percent2=watermazedata$Probe.Percent2,
# Probe.Percent3=watermazedata$Probe.Percent3,
# Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
# Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
# Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
# Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
# Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
# Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
# Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
# Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
# watermazedata$Treatment, describe)
# normality of overall variables
# shapiro.test(watermazedata$Duration.Cued)
# shapiro.test(watermazedata$Duration.Spatial1)
# shapiro.test(watermazedata$Duration.Spatial2)
# shapiro.test(watermazedata$Duration.Spatial3)
# shapiro.test(watermazedata$Duration.Spatial)
# shapiro.test(watermazedata$Distance.Cued)
# shapiro.test(watermazedata$Distance.Spatial1)
# shapiro.test(watermazedata$Distance.Spatial2)
# shapiro.test(watermazedata$Distance.Spatial3)
# shapiro.test(watermazedata$Distance.Spatial)
# shapiro.test(watermazedata$Speed)
# shapiro.test(watermazedata$Probe.Entries.1)
# shapiro.test(watermazedata$Probe.Entries.2)
# shapiro.test(watermazedata$Probe.Entries.3)
# shapiro.test(watermazedata$Probe.Entries.Ave)
# shapiro.test(watermazedata$Probe.Percent1)
# shapiro.test(watermazedata$Probe.Percent2)
# shapiro.test(watermazedata$Probe.Percent3)
# shapiro.test(watermazedata$Probe.Percent.Ave)
# shapiro.test(watermazedata$Probe2.Opposite.Percent)
# shapiro.test(watermazedata$Working.Duration.Trial1.Ave)
# shapiro.test(watermazedata$Working.Duration.Trial2.Ave)
# shapiro.test(watermazedata$Working.Duration.Diff.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial1.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial2.Ave)
# shapiro.test(watermazedata$Working.Distance.Diff.Ave)
# normality of variables broken down by group
# by(watermazedata$Duration.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Speed, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment, shapiro.test)Using stat.desc
# stat.desc()
# using basic = FALSE adds Shapiro-Wilks test, negating the need to run that separately as with describe()
# Overall DVs (not broken down by group)
stat.desc(cbind(Duration.Cued=watermazedata$Duration.Cued,
Duration.Spatial1=watermazedata$Duration.Spatial1,
Duration.Spatial2=watermazedata$Duration.Spatial2,
Duration.Spatial3=watermazedata$Duration.Spatial3,
Duration.Spatial=watermazedata$Duration.Spatial,
Distance.Cued=watermazedata$Distance.Cued,
Distance.Spatial1=watermazedata$Distance.Spatial1,
Distance.Spatial2=watermazedata$Distance.Spatial2,
Distance.Spatial3=watermazedata$Distance.Spatial3,
Distance.Spatial=watermazedata$Distance.Spatial,
Speed=watermazedata$Speed,
Probe.Entries.1=watermazedata$Probe.Entries.1,
Probe.Entries.2=watermazedata$Probe.Entries.2,
Probe.Entries.3=watermazedata$Probe.Entries.3,
Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
Probe.Percent1=watermazedata$Probe.Percent1,
Probe.Percent2=watermazedata$Probe.Percent2,
Probe.Percent3=watermazedata$Probe.Percent3,
Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
basic = FALSE, norm = TRUE)# or
# stat.desc(watermazedata[, c("Duration.Spatial1",
# "Duration.Spatial2",
# "Duration.Spatial3")], basic = FALSE, norm = TRUE); # ETC
# Broken down by group
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# multiple variables at once:
by(cbind(Duration.Cued=watermazedata$Duration.Cued,
Duration.Spatial1=watermazedata$Duration.Spatial1,
Duration.Spatial2=watermazedata$Duration.Spatial2,
Duration.Spatial3=watermazedata$Duration.Spatial3,
Duration.Spatial=watermazedata$Duration.Spatial,
Distance.Cued=watermazedata$Distance.Cued,
Distance.Spatial1=watermazedata$Distance.Spatial1,
Distance.Spatial2=watermazedata$Distance.Spatial2,
Distance.Spatial3=watermazedata$Distance.Spatial3,
Distance.Spatial=watermazedata$Distance.Spatial,
Speed=watermazedata$Speed,
Probe.Entries.1=watermazedata$Probe.Entries.1,
Probe.Entries.2=watermazedata$Probe.Entries.2,
Probe.Entries.3=watermazedata$Probe.Entries.3,
Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
Probe.Percent1=watermazedata$Probe.Percent1,
Probe.Percent2=watermazedata$Probe.Percent2,
Probe.Percent3=watermazedata$Probe.Percent3,
Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent),
watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)INDICES: Ac
Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median 22.3900000 14.36000000 15.54000000 17.19000000
mean 23.6470000 14.98000000 18.94100000 21.35800000
SE.mean 1.6968797 1.77887487 2.27763010 2.43156865
CI.mean.0.95 3.5516099 3.72322788 4.76713460 5.08933168
var 57.5880116 63.28791579 103.75197789 118.25052211
std.dev 7.5886765 7.95537025 10.18587148 10.87430559
coef.var 0.3209150 0.53106610 0.53776841 0.50914438
skewness 0.2479948 1.26212085 0.94110222 0.73566280
skew.2SE 0.2421335 1.23229118 0.91885968 0.71827573
kurtosis -1.1792491 1.97484499 0.31484533 -0.60321900
kurt.2SE -0.5941498 0.99500081 0.15863086 -0.30392430
normtest.W 0.9608703 0.87806268 0.91348508 0.91465100
normtest.p 0.5613788 0.01633146 0.07429047 0.07820961
Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median 18.0066667 3.4727000 2.19760000 2.4629000
mean 18.4263333 3.6469100 2.34443000 2.7195600
SE.mean 1.0939434 0.2324343 0.34439751 0.3433361
CI.mean.0.95 2.2896498 0.4864905 0.72083228 0.7186106
var 23.9342432 1.0805138 2.37219293 2.3575929
std.dev 4.8922636 1.0394776 1.54019250 1.5354455
coef.var 0.2655039 0.2850297 0.65695819 0.5645934
skewness 0.2518218 0.5425664 1.34133180 0.8403708
skew.2SE 0.2458701 0.5297431 1.30963002 0.8205091
kurtosis -0.5840827 -0.8509631 2.25394485 0.3936414
kurt.2SE -0.2942827 -0.4287470 1.13562176 0.1983313
normtest.W 0.9801489 0.9417958 0.87785717 0.9280982
normtest.p 0.9360099 0.2592150 0.01619371 0.1419282
Distance.Spatial3 Distance.Spatial Speed Probe.Entries.1
median 2.34980000 2.6594667 0.1444310179 3.5000000
mean 3.08606000 2.7166833 0.1467410626 3.7000000
SE.mean 0.40930437 0.1810658 0.0023242164 0.4298102
CI.mean.0.95 0.85668390 0.3789751 0.0048646408 0.8996032
var 3.35060138 0.6556967 0.0001080396 3.6947368
std.dev 1.83046480 0.8097510 0.0103942117 1.9221698
coef.var 0.59313973 0.2980660 0.0708336951 0.5195054
skewness 0.70812629 0.6477020 -0.0297399089 0.2374009
skew.2SE 0.69139004 0.6323939 -0.0290370192 0.2317900
kurtosis -0.94451685 0.4876389 -0.4892196484 -0.4419827
kurt.2SE -0.47588294 0.2456907 -0.2464871660 -0.2226874
normtest.W 0.88665782 0.9401439 0.9760920947 0.9704263
normtest.p 0.02335664 0.2412871 0.8743843994 0.7638524
Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median 5.0000000 5.0000000 5.00000000 33.58500000
mean 5.4000000 4.8500000 4.60000000 34.28200000
SE.mean 0.6341177 0.5585460 0.29379549 3.17795114
CI.mean.0.95 1.3272236 1.1690503 0.61492103 6.65152819
var 8.0421053 6.2394737 1.72631579 201.98746947
std.dev 2.8358606 2.4978938 1.31389337 14.21222957
coef.var 0.5251594 0.5150297 0.28562899 0.41456827
skewness 0.7176968 0.3927197 -0.47085901 -0.05372888
skew.2SE 0.7007343 0.3834380 -0.45973047 -0.05245902
kurtosis -0.3181295 -0.3691493 -0.48631469 -1.14227338
kurt.2SE -0.1602855 -0.1859912 -0.24502354 -0.57552007
normtest.W 0.9332753 0.9271466 0.90536342 0.97187672
normtest.p 0.1784997 0.1360601 0.05203957 0.79392004
Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median 41.25000000 36.50000000 36.7500000
mean 39.20000000 33.88300000 35.7890000
SE.mean 2.56736441 1.85310608 1.2512964
CI.mean.0.95 5.37355546 3.87859561 2.6189934
var 131.82720000 68.68004316 31.3148516
std.dev 11.48160268 8.28734235 5.5959674
coef.var 0.29289803 0.24458703 0.1563600
skewness -0.94601833 -0.95143892 -0.4572726
skew.2SE -0.92365960 -0.92895208 -0.4464651
kurtosis 0.29968759 -0.21830216 -0.4986564
kurt.2SE 0.15099382 -0.10998880 -0.2512418
normtest.W 0.91907919 0.88425152 0.9543853
normtest.p 0.09512401 0.02111589 0.4385766
Probe2.Opposite.Percent
median 18.0000000
mean 20.2990000
SE.mean 1.5917789
CI.mean.0.95 3.3316314
var 50.6751989
std.dev 7.1186515
coef.var 0.3506898
skewness 0.6760278
skew.2SE 0.6600502
kurtosis -0.5215679
kurt.2SE -0.2627854
normtest.W 0.9295934
normtest.p 0.1516587
---------------------------------------------------------------
INDICES: Fx
Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median 21.73000000 12.4300000 1.755000e+01 21.40000000
mean 23.37222222 12.7988889 2.153222e+01 23.43777778
SE.mean 2.23278843 1.2639398 2.967962e+00 3.15191648
CI.mean.0.95 4.71077180 2.6666798 6.261853e+00 6.64996249
var 89.73619477 28.7557869 1.585584e+02 178.82239477
std.dev 9.47291902 5.3624423 1.259200e+01 13.37244909
coef.var 0.40530673 0.4189772 5.847979e-01 0.57055107
skewness 0.81041711 0.3882022 1.272045e+00 0.50503295
skew.2SE 0.75559436 0.3619413 1.185994e+00 0.47086870
kurtosis 0.10639342 -1.0798882 7.314238e-01 -1.16154284
kurt.2SE 0.05125936 -0.5202800 3.523932e-01 -0.55962051
normtest.W 0.86919556 0.9360768 8.435289e-01 0.90640023
normtest.p 0.01725848 0.2480028 6.706633e-03 0.07435655
Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median 19.99666667 3.69610000 1.62460000 2.210800000
mean 19.25629630 3.98997778 1.98138889 2.894511111
SE.mean 1.42361882 0.38879126 0.26145987 0.425809855
CI.mean.0.95 3.00357317 0.82027787 0.55163210 0.898380264
var 36.48042992 2.72085565 1.23050271 3.263652580
std.dev 6.03990314 1.64950164 1.10928027 1.806558214
coef.var 0.31365861 0.41341123 0.55984985 0.624132416
skewness -0.18478275 0.62065506 0.67394281 1.130473609
skew.2SE -0.17228264 0.57866925 0.62835222 1.053999812
kurtosis -1.68798788 -0.23498226 -0.86690831 0.205166745
kurt.2SE -0.81325683 -0.11321226 -0.41766834 0.098847426
normtest.W 0.88880514 0.88847894 0.90114980 0.846707522
normtest.p 0.03681698 0.03634723 0.06017156 0.007517738
Distance.Spatial3 Distance.Spatial Speed Probe.Entries.1
median 2.94920000 2.8016333 0.134896814 4.0000000
mean 3.16536667 2.6804222 0.138482061 4.3333333
SE.mean 0.48732298 0.2206222 0.004480923 0.7094138
CI.mean.0.95 1.02816161 0.4654721 0.009453920 1.4967323
var 4.27470629 0.8761347 0.000361416 9.0588235
std.dev 2.06753628 0.9360207 0.019010944 3.0097880
coef.var 0.65317434 0.3492064 0.137280918 0.6945665
skewness 0.44035925 -0.1121716 -0.109850417 0.5406443
skew.2SE 0.41057002 -0.1045835 -0.102419303 0.5040710
kurtosis -1.29548429 -1.3864203 -1.287317657 -0.7907476
kurt.2SE -0.62415226 -0.6679644 -0.620217651 -0.3809748
normtest.W 0.89269316 0.9480545 0.956655741 0.9386126
normtest.p 0.04293112 0.3953187 0.538577561 0.2743302
Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median 4.5000000 4.00000000 4.00000000 35.41500000
mean 4.6111111 3.61111111 4.16666667 39.11166667
SE.mean 0.5724554 0.38039272 0.45911253 3.49474089
CI.mean.0.95 1.2077753 0.80255848 0.96864276 7.37325877
var 5.8986928 2.60457516 3.79411765 219.83785000
std.dev 2.4287225 1.61386962 1.94784949 14.82692989
coef.var 0.5267109 0.44691774 0.46748388 0.37909225
skewness 0.8494462 -0.26808635 0.63646389 1.06294974
skew.2SE 0.7919832 -0.24995096 0.59340865 0.99104377
kurtosis 0.3982610 0.03711183 0.09433583 0.16236708
kurt.2SE 0.1918785 0.01788013 0.04545013 0.07822695
normtest.W 0.9326035 0.94329048 0.91525179 0.88015311
normtest.p 0.2157300 0.32956448 0.10652068 0.02625901
Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median 35.50000000 30.5000000 34.775000000
mean 37.07388889 28.4633333 34.882222222
SE.mean 2.92394623 1.9470360 1.891559435
CI.mean.0.95 6.16898729 4.1078868 3.990841562
var 153.89030752 68.2370824 64.403947712
std.dev 12.40525322 8.2605740 8.025207020
coef.var 0.33460890 0.2902181 0.230065819
skewness 0.08594422 -0.9333001 -0.006024109
skew.2SE 0.08013030 -0.8701646 -0.005616592
kurtosis -0.57837040 0.3594684 -0.002432449
kurt.2SE -0.27865347 0.1731885 -0.001171931
normtest.W 0.97685565 0.9194261 0.979925163
normtest.p 0.91192507 0.1262816 0.949237025
Probe2.Opposite.Percent
median 18.58000000
mean 20.13000000
SE.mean 2.23206915
CI.mean.0.95 4.70925426
var 89.67838824
std.dev 9.46986738
coef.var 0.47043554
skewness 0.70679512
skew.2SE 0.65898214
kurtosis 0.05188936
kurt.2SE 0.02499981
normtest.W 0.94387323
normtest.p 0.33708159
---------------------------------------------------------------
INDICES: Sh
Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median 23.06000000 12.08000000 15.5400000 22.4200000
mean 25.09789474 13.82210526 19.0052632 25.8894737
SE.mean 2.63775418 1.50982350 1.8363196 2.4306471
CI.mean.0.95 5.54171590 3.17202147 3.8579644 5.1066001
var 132.19719532 43.31177310 64.0693263 112.2528608
std.dev 11.49770392 6.58116806 8.0043317 10.5949451
coef.var 0.45811428 0.47613355 0.4211640 0.4092376
skewness 0.79198208 0.83572880 0.4171269 0.6594636
skew.2SE 0.75604471 0.79780636 0.3981992 0.6295394
kurtosis -0.49113414 -0.51161321 -1.1723783 0.9363186
kurt.2SE -0.24211217 -0.25220764 -0.5779420 0.4615727
normtest.W 0.90845887 0.88784203 0.9197108 0.9276883
normtest.p 0.06936469 0.02947699 0.1117835 0.1569926
Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median 18.8733333 3.31400000 2.33220000 2.36080000
mean 19.5722807 3.91464211 2.17807368 2.73778947
SE.mean 1.0525935 0.42612757 0.26426884 0.29181399
CI.mean.0.95 2.2114168 0.89526079 0.55520824 0.61307845
var 21.0511075 3.45010934 1.32692242 1.61795274
std.dev 4.5881486 1.85744699 1.15192119 1.27198771
coef.var 0.2344207 0.47448705 0.52887154 0.46460392
skewness 0.5303294 0.89863537 0.76850264 0.61738279
skew.2SE 0.5062650 0.85785845 0.73363069 0.58936812
kurtosis -0.3497901 -0.42806821 -0.27891088 -0.03764729
kurt.2SE -0.1724345 -0.21102284 -0.13749343 -0.01855881
normtest.W 0.9634343 0.87435266 0.90134888 0.95201279
normtest.p 0.6416063 0.01714434 0.05146746 0.42730327
Distance.Spatial3 Distance.Spatial Speed Probe.Entries.1
median 3.80020000 2.79413333 0.1534098787 5.0000000000
mean 3.98161053 2.96582456 0.1507638122 4.1578947368
SE.mean 0.40007061 0.20119798 0.0037067999 0.5029919715
CI.mean.0.95 0.84051716 0.42270127 0.0077876976 1.0567469189
var 3.04107338 0.76913191 0.0002610669 4.8070175439
std.dev 1.74386736 0.87700166 0.0161575662 2.1924911730
coef.var 0.43798040 0.29570247 0.1071713824 0.5273080036
skewness 0.27448847 1.30140677 -0.3163322606 -0.8492536425
skew.2SE 0.26203314 1.24235349 -0.3019782125 -0.8107174918
kurtosis 0.04990615 1.89038983 0.4644608256 -0.7281113771
kurt.2SE 0.02460201 0.93189689 0.2289631446 -0.3589337600
normtest.W 0.98061264 0.87112946 0.9445009017 0.7872014119
normtest.p 0.94920809 0.01509693 0.3172627904 0.0007493052
Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median 6.00000000 4.0000000 5.00000000 32.3300000
mean 6.42105263 4.0526316 4.78947368 33.2978947
SE.mean 0.65923839 0.5897451 0.38676154 3.2100484
CI.mean.0.95 1.38500847 1.2390085 0.81255584 6.7440615
var 8.25730994 6.6081871 2.84210526 195.7838064
std.dev 2.87355354 2.5706394 1.68585446 13.9922767
coef.var 0.44752063 0.6343136 0.35199159 0.4202151
skewness 0.38337532 0.2609263 0.51064922 -0.5070319
skew.2SE 0.36597909 0.2490864 0.48747775 -0.4840245
kurtosis -1.23833007 -1.5641787 -1.00939320 -0.4344221
kurt.2SE -0.61045395 -0.7710861 -0.49759598 -0.2141551
normtest.W 0.89668584 0.8792907 0.88283992 0.9530587
normtest.p 0.04239384 0.0208689 0.02406722 0.4447327
Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median 43.1700000 31.3300000 34.22000000
mean 42.3352632 31.9468421 35.86052632
SE.mean 2.1079617 2.2356719 1.64424594
CI.mean.0.95 4.4286632 4.6969723 3.45443254
var 84.4265485 94.9663450 51.36734971
std.dev 9.1883921 9.7450677 7.16710190
coef.var 0.2170387 0.3050401 0.19986048
skewness -0.3808935 0.5854291 -0.09526113
skew.2SE -0.3636099 0.5588644 -0.09093851
kurtosis -0.9750009 -0.8419064 -1.24496141
kurt.2SE -0.4806418 -0.4150308 -0.61372297
normtest.W 0.9472765 0.9320952 0.96071802
normtest.p 0.3548309 0.1892693 0.58660667
Probe2.Opposite.Percent
median 17.3300000
mean 17.7100000
SE.mean 1.2207154
CI.mean.0.95 2.5646280
var 28.3127778
std.dev 5.3209753
coef.var 0.3004503
skewness 0.4735778
skew.2SE 0.4520885
kurtosis -0.7236955
kurt.2SE -0.3567569
normtest.W 0.9488611
normtest.p 0.3778937# or
# by(watermazedata[, c("Duration.Spatial1",
# "Duration.Spatial1",
# "Duration.Spatial3")],; #ETC
# watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
Test for homogeneity of variance among groups using the leveneTest() function from the car package (default uses median)… to use mean instead of median - for example: leveneTest(watermazedata\(Duration.Spatial, watermazedata\)Treatment, center = mean)
leveneTest(watermazedata$Duration.Cued, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.9112 0.4081
54 leveneTest(watermazedata$Duration.Spatial1, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.3835 0.6833
54 leveneTest(watermazedata$Duration.Spatial2, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.363 0.6973
54 leveneTest(watermazedata$Duration.Spatial3, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.9983 0.3752
54 leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 2.4787 0.09335 .
54
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1leveneTest(watermazedata$Distance.Cued, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 1.2023 0.3084
54 leveneTest(watermazedata$Distance.Spatial1, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.1913 0.8265
54 leveneTest(watermazedata$Distance.Spatial2, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.4073 0.6675
54 leveneTest(watermazedata$Distance.Spatial3, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.5724 0.5676
54 leveneTest(watermazedata$Distance.Spatial, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.9525 0.3921
54 leveneTest(watermazedata$Speed, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 3.4883 0.0376 *
54
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1leveneTest(watermazedata$Probe.Entries.1, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 1.4818 0.2363
54 leveneTest(watermazedata$Probe.Entries.2, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.2239 0.8002
54 leveneTest(watermazedata$Probe.Entries.3, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 2.231 0.1172
54 leveneTest(watermazedata$Probe.Entries.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.9072 0.4097
54 leveneTest(watermazedata$Probe.Percent1, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.0387 0.962
54 leveneTest(watermazedata$Probe.Percent2, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.3962 0.6748
54 leveneTest(watermazedata$Probe.Percent3, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.4923 0.6139
54 leveneTest(watermazedata$Probe.Percent.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.9529 0.392
54 leveneTest(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 1.456 0.2422
54 leveneTest(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.1796 0.8361
54 leveneTest(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.1042 0.9012
54 leveneTest(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 1.123 0.3328
54 leveneTest(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.3452 0.7096
54 leveneTest(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.0259 0.9745
54 leveneTest(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment)watermazedata$Treatment coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 0.8803 0.4205
54 # *** IF 1 OF 3 IS SIGNIFICANT [NOT NORMAL], DOES THAT REQUIRE NONPARAMETRIC? ***Visually check each variable’s data for normality / outliers averaged across all groups. This info is probably not all that interestng until broken down by group. - Histograms w/ overlaid normal curves - Quantile–quantile (QQ) plots - Boxplots - Scatterplots - Violin plots
# Histograms with overlaid normal curve and Quantile–quantile plots
# Scatterplots:
# p <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
# use geom_crossbar()
# p + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
# Use geom_errorbar()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="errorbar", color="red", width=0.2) + stat_summary(fun.y=mean, geom="point", color="red")
# Use geom_pointrange()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="pointrange", color="red")
# Duration
hist.Duration.Cued <- ggplot(watermazedata, aes(Duration.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Duration.Cued, na.rm = TRUE),
sd = sd(watermazedata$Duration.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial1 <- ggplot(watermazedata, aes(Duration.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Duration.Spatial1, na.rm = TRUE),
sd = sd(watermazedata$Duration.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial2 <- ggplot(watermazedata, aes(Duration.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Duration.Spatial2, na.rm = TRUE),
sd = sd(watermazedata$Duration.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial3 <- ggplot(watermazedata, aes(Duration.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Duration.Spatial3, na.rm = TRUE),
sd = sd(watermazedata$Duration.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial <- ggplot(watermazedata, aes(Duration.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Duration.Spatial, na.rm = TRUE),
sd = sd(watermazedata$Duration.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Distance
hist.Distance.Cued <- ggplot(watermazedata, aes(Distance.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Distance.Cued, na.rm = TRUE),
sd = sd(watermazedata$Distance.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial1 <- ggplot(watermazedata, aes(Distance.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Distance.Spatial1, na.rm = TRUE),
sd = sd(watermazedata$Distance.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial2 <- ggplot(watermazedata, aes(Distance.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Distance.Spatial2, na.rm = TRUE),
sd = sd(watermazedata$Distance.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial3 <- ggplot(watermazedata, aes(Distance.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Distance.Spatial3, na.rm = TRUE),
sd = sd(watermazedata$Distance.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial <- ggplot(watermazedata, aes(Distance.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Distance.Spatial, na.rm = TRUE),
sd = sd(watermazedata$Distance.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Speed
hist.Speed <- ggplot(watermazedata, aes(Speed)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Speed", y = "Number")
hist.Speed +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Speed, na.rm = TRUE),
sd = sd(watermazedata$Speed, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Speed <- qplot(sample = watermazedata$Speed)
qqplot.Speed
boxplot(watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Probe stuff
hist.Probe.Entries.1 <- ggplot(watermazedata, aes(Probe.Entries.1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Entries.1, na.rm = TRUE),
sd = sd(watermazedata$Probe.Entries.1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.2 <- ggplot(watermazedata, aes(Probe.Entries.2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Entries.2, na.rm = TRUE),
sd = sd(watermazedata$Probe.Entries.2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.3 <- ggplot(watermazedata, aes(Probe.Entries.3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Entries.3, na.rm = TRUE),
sd = sd(watermazedata$Probe.Entries.3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.Ave <- ggplot(watermazedata, aes(Probe.Entries.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Entries.Ave, na.rm = TRUE),
sd = sd(watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent1 <- ggplot(watermazedata, aes(Probe.Percent1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Percent1, na.rm = TRUE),
sd = sd(watermazedata$Probe.Percent1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent2 <- ggplot(watermazedata, aes(Probe.Percent2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Percent2, na.rm = TRUE),
sd = sd(watermazedata$Probe.Percent2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent3 <- ggplot(watermazedata, aes(Probe.Percent3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Percent3, na.rm = TRUE),
sd = sd(watermazedata$Probe.Percent3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent.Ave <- ggplot(watermazedata, aes(Probe.Percent.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe.Percent.Ave, na.rm = TRUE),
sd = sd(watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe2.Opposite.Percent <- ggplot(watermazedata, aes(Probe2.Opposite.Percent)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
sd = sd(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Working memory stuff
hist.Working.Duration.Trial1.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Trial2.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Diff.Ave <- ggplot(watermazedata, aes(Working.Duration.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial1.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial2.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Diff.Ave <- ggplot(watermazedata, aes(Working.Distance.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
sd = sd(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
Now, visually check each variable data for normality / outliers broken down by group - histograms and QQ plots and box plots for each Treatment group.
# Broken down by group (use the "subset" dataframes that were derived earlier)
# Ac
# Duration
hist.Duration.Cued <- ggplot(Ac_watermazedata, aes(Duration.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Duration.Cued, na.rm = TRUE),
sd = sd(Ac_watermazedata$Duration.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Ac_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Ac_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial1 <- ggplot(Ac_watermazedata, aes(Duration.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE),
sd = sd(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Ac_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Ac_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial2 <- ggplot(Ac_watermazedata, aes(Duration.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE),
sd = sd(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Ac_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Ac_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial3 <- ggplot(Ac_watermazedata, aes(Duration.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE),
sd = sd(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Ac_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Ac_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial <- ggplot(Ac_watermazedata, aes(Duration.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Duration.Spatial, na.rm = TRUE),
sd = sd(Ac_watermazedata$Duration.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Ac_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Ac_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Distance
hist.Distance.Cued <- ggplot(Ac_watermazedata, aes(Distance.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Distance.Cued, na.rm = TRUE),
sd = sd(Ac_watermazedata$Distance.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Ac_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Ac_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial1 <- ggplot(Ac_watermazedata, aes(Distance.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE),
sd = sd(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Ac_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Ac_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial2 <- ggplot(Ac_watermazedata, aes(Distance.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE),
sd = sd(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Ac_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Ac_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial3 <- ggplot(Ac_watermazedata, aes(Distance.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE),
sd = sd(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Ac_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Ac_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial <- ggplot(Ac_watermazedata, aes(Distance.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Distance.Spatial, na.rm = TRUE),
sd = sd(Ac_watermazedata$Distance.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Ac_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Ac_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Speed
hist.Speed <- ggplot(Ac_watermazedata, aes(Speed)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Speed", y = "Number")
hist.Speed +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Speed, na.rm = TRUE),
sd = sd(Ac_watermazedata$Speed, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Ac_watermazedata$Speed)
qqplot.Speed
boxplot(Ac_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Probe stuff
hist.Probe.Entries.1 <- ggplot(Ac_watermazedata, aes(Probe.Entries.1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Ac_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Ac_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.2 <- ggplot(Ac_watermazedata, aes(Probe.Entries.2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Ac_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Ac_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.3 <- ggplot(Ac_watermazedata, aes(Probe.Entries.3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Ac_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Ac_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.Ave <- ggplot(Ac_watermazedata, aes(Probe.Entries.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Ac_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Ac_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent1 <- ggplot(Ac_watermazedata, aes(Probe.Percent1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Percent1, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Percent1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Ac_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Ac_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent2 <- ggplot(Ac_watermazedata, aes(Probe.Percent2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Percent2, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Percent2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Ac_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Ac_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent3 <- ggplot(Ac_watermazedata, aes(Probe.Percent3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Percent3, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Percent3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Ac_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Ac_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent.Ave <- ggplot(Ac_watermazedata, aes(Probe.Percent.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Ac_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Ac_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe2.Opposite.Percent <- ggplot(Ac_watermazedata, aes(Probe2.Opposite.Percent)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
sd = sd(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Ac_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Ac_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Working memory stuff
hist.Working.Duration.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Ac_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
sd = sd(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Ac_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Fx
# Duration
hist.Duration.Cued <- ggplot(Fx_watermazedata, aes(Duration.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Duration.Cued, na.rm = TRUE),
sd = sd(Fx_watermazedata$Duration.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Fx_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Fx_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial1 <- ggplot(Fx_watermazedata, aes(Duration.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE),
sd = sd(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Fx_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Fx_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial2 <- ggplot(Fx_watermazedata, aes(Duration.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE),
sd = sd(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Fx_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Fx_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial3 <- ggplot(Fx_watermazedata, aes(Duration.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE),
sd = sd(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Fx_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Fx_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial <- ggplot(Fx_watermazedata, aes(Duration.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Duration.Spatial, na.rm = TRUE),
sd = sd(Fx_watermazedata$Duration.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Fx_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Fx_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Distance
hist.Distance.Cued <- ggplot(Fx_watermazedata, aes(Distance.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Distance.Cued, na.rm = TRUE),
sd = sd(Fx_watermazedata$Distance.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Fx_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Fx_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial1 <- ggplot(Fx_watermazedata, aes(Distance.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE),
sd = sd(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Fx_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Fx_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial2 <- ggplot(Fx_watermazedata, aes(Distance.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE),
sd = sd(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Fx_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Fx_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial3 <- ggplot(Fx_watermazedata, aes(Distance.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE),
sd = sd(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Fx_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Fx_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial <- ggplot(Fx_watermazedata, aes(Distance.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Distance.Spatial, na.rm = TRUE),
sd = sd(Fx_watermazedata$Distance.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Fx_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Fx_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Speed
hist.Speed <- ggplot(Fx_watermazedata, aes(Speed)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Speed", y = "Number")
hist.Speed +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Speed, na.rm = TRUE),
sd = sd(Fx_watermazedata$Speed, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Fx_watermazedata$Speed)
qqplot.Speed
boxplot(Fx_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Probe stuff
hist.Probe.Entries.1 <- ggplot(Fx_watermazedata, aes(Probe.Entries.1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Fx_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Fx_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.2 <- ggplot(Fx_watermazedata, aes(Probe.Entries.2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Fx_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Fx_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.3 <- ggplot(Fx_watermazedata, aes(Probe.Entries.3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Fx_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Fx_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.Ave <- ggplot(Fx_watermazedata, aes(Probe.Entries.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Fx_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Fx_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent1 <- ggplot(Fx_watermazedata, aes(Probe.Percent1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Percent1, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Percent1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Fx_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Fx_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent2 <- ggplot(Fx_watermazedata, aes(Probe.Percent2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Percent2, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Percent2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Fx_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Fx_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent3 <- ggplot(Fx_watermazedata, aes(Probe.Percent3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Percent3, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Percent3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Fx_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Fx_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent.Ave <- ggplot(Fx_watermazedata, aes(Probe.Percent.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Fx_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Fx_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe2.Opposite.Percent <- ggplot(Fx_watermazedata, aes(Probe2.Opposite.Percent)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
sd = sd(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Fx_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Fx_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Working memory stuff
hist.Working.Duration.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Fx_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
sd = sd(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Fx_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Sh
# Duration
hist.Duration.Cued <- ggplot(Sh_watermazedata, aes(Duration.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Duration.Cued, na.rm = TRUE),
sd = sd(Sh_watermazedata$Duration.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Sh_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Sh_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial1 <- ggplot(Sh_watermazedata, aes(Duration.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE),
sd = sd(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Sh_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Sh_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial2 <- ggplot(Sh_watermazedata, aes(Duration.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE),
sd = sd(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Sh_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Sh_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial3 <- ggplot(Sh_watermazedata, aes(Duration.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE),
sd = sd(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Sh_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Sh_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Duration.Spatial <- ggplot(Sh_watermazedata, aes(Duration.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Duration.Spatial, na.rm = TRUE),
sd = sd(Sh_watermazedata$Duration.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Sh_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Sh_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Distance
hist.Distance.Cued <- ggplot(Sh_watermazedata, aes(Distance.Cued)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Distance.Cued, na.rm = TRUE),
sd = sd(Sh_watermazedata$Distance.Cued, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Sh_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Sh_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial1 <- ggplot(Sh_watermazedata, aes(Distance.Spatial1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE),
sd = sd(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Sh_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Sh_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial2 <- ggplot(Sh_watermazedata, aes(Distance.Spatial2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE),
sd = sd(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Sh_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Sh_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial3 <- ggplot(Sh_watermazedata, aes(Distance.Spatial3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE),
sd = sd(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Sh_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Sh_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Distance.Spatial <- ggplot(Sh_watermazedata, aes(Distance.Spatial)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Distance.Spatial, na.rm = TRUE),
sd = sd(Sh_watermazedata$Distance.Spatial, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Sh_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Sh_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Speed
hist.Speed <- ggplot(Sh_watermazedata, aes(Speed)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Speed", y = "Number")
hist.Speed +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Speed, na.rm = TRUE),
sd = sd(Sh_watermazedata$Speed, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Sh_watermazedata$Speed)
qqplot.Speed
boxplot(Sh_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Probe stuff
hist.Probe.Entries.1 <- ggplot(Sh_watermazedata, aes(Probe.Entries.1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Sh_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Sh_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.2 <- ggplot(Sh_watermazedata, aes(Probe.Entries.2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Sh_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Sh_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.3 <- ggplot(Sh_watermazedata, aes(Probe.Entries.3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Sh_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Sh_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Entries.Ave <- ggplot(Sh_watermazedata, aes(Probe.Entries.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Sh_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Sh_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent1 <- ggplot(Sh_watermazedata, aes(Probe.Percent1)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Percent1, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Percent1, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Sh_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Sh_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent2 <- ggplot(Sh_watermazedata, aes(Probe.Percent2)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Percent2, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Percent2, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Sh_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Sh_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent3 <- ggplot(Sh_watermazedata, aes(Probe.Percent3)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Percent3, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Percent3, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Sh_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Sh_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe.Percent.Ave <- ggplot(Sh_watermazedata, aes(Probe.Percent.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Sh_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Sh_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Probe2.Opposite.Percent <- ggplot(Sh_watermazedata, aes(Probe2.Opposite.Percent)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
sd = sd(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Sh_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Sh_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Working memory stuff
hist.Working.Duration.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Duration.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Sh_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial1.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial2.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
hist.Working.Distance.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Diff.Ave)) +
geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
stat_function(fun = dnorm, args = list
(mean = mean(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
sd = sd(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Sh_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
Now visually compare groups against each other
# Broken down by group all on 1 graph
# Duration
ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Distance
ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Speed
ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Probe stuff
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
# Working memory stuff
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Violin chart") +
xlab("")
Meeting assumptions of normality / homogeneity of variance can be tough w/ large data sets because small variations can be “significant” (you can also test homogeneity of variance w/ “variance ratio” or Hartley’s Fmax). Either way, if data are not normally distributed and of equal variances, parametric tests are not valid. To correct “problems” with the data:
Outliers - remove the case / subject (especially if it was somehow “different”) - “bring the case it into the fold” using the mean + 2 or 3 SDs - Change the score to be the mean + 2 or 3 SDs - “bring the case into the fold” using the next highest score plus one method - Change the score to be one unit above the next highest score in the data set
For non-normally-distributed data: - Can also use “trimmed means” (removing a specific % of cases have been removed from each end) - Can also use “M-estimator” which empirically derives the proper % to trim - Can also use bootstrapping to estimate “true” mean / variance - Transform the data: log, square root, or reciprocal transformations can correct for positive skew and/or unequal variance. If data are negatively skewed, you need derive a reciprocal score (reverse the scores by subtracting each score from the highest score obtained) – Make new transformed DVs using newVariable <- function(oldVariable) — Square root: watermazedata\(Duration.Spatial.Sqrt <- sqrt(watermazedata\)Duration.Spatial) — Absolute value: watermazedata\(Duration.Spatial.Abs <- abs(watermazedata\)Duration.Spatial.Diff) — Log (natural): watermazedata\(Duration.Spatial.Log <- log(watermazedata\)Duration.Spatial +1) +1 needed to avoid trying to calculate log of 0 — Log (base 10): watermazedata\(Duration.Spatial.Log10 <- log10(watermazedata\)Duration.Spatial) +1 needed for base 10??? — Reciprocal: watermazedata\(Duration.Spatial.Reciprocal <- 1/(watermazedata\)Duration.Spatial +1) +1 needed to avoid trying to divide by zero
---
title: "R Notebook - EDA workflow template using Nelson data"
author: "Rich Hartman, PhD"
date: "December 18th, 2019"
output:
  html_notebook: default
  word_document: default
---

install required packages (only need to do this once?)

install.packages("ggplot2"); # for graphics functions
install.packages("car"); # for the leveneTest() function
install.packages("pastecs"); # for the stat.desc() function
install.packages("psych"); # for the describe() function
install.packages("hrbrthemes")
install.packages("viridis")

"call" the required packages (need to do this every session?)

```{r}
library(car); library(ggplot2); library(pastecs); library(psych); library(hrbrthemes); library(viridis)
```

Use Excel to generate a .csv file with "tidy" data (each row = 1 case / subject, 1st row is column names). Import .CSV file into R "dataframe" called "watermazedata". Then show the "watermazedata" dataframe (header + 1st 8 data rows) to check it out

```{r}
watermazedata <- read.csv(file="./data_clean/water maze all.csv", header=TRUE, sep=",")
watermazedata
```

Derive new variables. Mostly use the rowMeans() function, but these may be useful as well...

Make some new DVs (~"columns") assigned 1 (TRUE) or 0 (FALSE) based on Boolean calculations:
- Less than? watermazedata$Duration.Spatial2LessThanSpatial1 <- watermazedata$Duration.Spatial2 < watermazedata$Duration.Spatial2
- Less than or equal to? watermazedata$Duration.Spatial1LessThanOrEqualTo60 <- watermazedata$Duration.Spatial2 <= 60
- Equal to? watermazedata$Sh <- watermazedata$Treatment == "Sh"
- Not equal to? watermazedata$NotSh <- watermazedata$Treatment != "Sh"

Use "ifelse" to maybe replace scores (e.g., replace any duration greater than 60 with NA "missing data", else keep the same)
- watermazedata$Duration.Spatial.Clean <- ifelse(watermazedata$Duration.Spatial > 60, NA, watermazedata$Duration.Spatial)

```{r}
# Trials 1-10 for cued, spatial 1, spatial 2 and spatial trials averaged into 5 blocks (2 trials each) each for both Distance and Duration
watermazedata$Duration.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.1,
                                                 watermazedata$Duration.Cued.2),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.3,
                                                 watermazedata$Duration.Cued.4),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.5,
                                                 watermazedata$Duration.Cued.6),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.7,
                                                 watermazedata$Duration.Cued.8),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.9,
                                                 watermazedata$Duration.Cued.10),
                                               na.rm = TRUE)

watermazedata$Duration.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.1,
                                                     watermazedata$Duration.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.3,
                                                     watermazedata$Duration.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.5,
                                                     watermazedata$Duration.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.7,
                                                     watermazedata$Duration.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.9,
                                                     watermazedata$Duration.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.1,
                                                     watermazedata$Duration.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.3,
                                                     watermazedata$Duration.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.5,
                                                     watermazedata$Duration.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.7,
                                                     watermazedata$Duration.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.9,
                                                     watermazedata$Duration.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.1,
                                                     watermazedata$Duration.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.3,
                                                     watermazedata$Duration.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.5,
                                                     watermazedata$Duration.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.7,
                                                     watermazedata$Duration.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.9,
                                                     watermazedata$Duration.Spatial3.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.1,
                                                 watermazedata$Distance.Cued.2),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.3,
                                                 watermazedata$Distance.Cued.4),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.5,
                                                 watermazedata$Distance.Cued.6),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.7,
                                                 watermazedata$Distance.Cued.8),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.9,
                                                 watermazedata$Distance.Cued.10),
                                               na.rm = TRUE)

watermazedata$Distance.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.1,
                                                     watermazedata$Distance.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.3,
                                                     watermazedata$Distance.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.5,
                                                     watermazedata$Distance.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.7,
                                                     watermazedata$Distance.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.9,
                                                     watermazedata$Distance.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.1,
                                                     watermazedata$Distance.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.3,
                                                     watermazedata$Distance.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.5,
                                                     watermazedata$Distance.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.7,
                                                     watermazedata$Distance.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.9,
                                                     watermazedata$Distance.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.1,
                                                     watermazedata$Distance.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.3,
                                                     watermazedata$Distance.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.5,
                                                     watermazedata$Distance.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.7,
                                                     watermazedata$Distance.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.9,
                                                     watermazedata$Distance.Spatial3.10),
                                                   na.rm = TRUE)

# Cued, spatial 1, spatial 2 and spatial blocks 1-5 averaged into Overall Averages for both Distance and Duration
watermazedata$Duration.Cued <- rowMeans(cbind (watermazedata$Duration.Cued.1,
                                               watermazedata$Duration.Cued.2,
                                               watermazedata$Duration.Cued.3,
                                               watermazedata$Duration.Cued.4,
                                               watermazedata$Duration.Cued.5),
                                        na.rm = TRUE)
watermazedata$Duration.Spatial1 <- rowMeans(cbind (watermazedata$Duration.Spatial1.1,
                                                   watermazedata$Duration.Spatial1.2,
                                                   watermazedata$Duration.Spatial1.3,
                                                   watermazedata$Duration.Spatial1.4,
                                                   watermazedata$Duration.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial2 <- rowMeans(cbind (watermazedata$Duration.Spatial2.1,
                                                   watermazedata$Duration.Spatial2.2,
                                                   watermazedata$Duration.Spatial2.3,
                                                   watermazedata$Duration.Spatial2.4,
                                                   watermazedata$Duration.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial3 <- rowMeans(cbind (watermazedata$Duration.Spatial3.1,
                                                   watermazedata$Duration.Spatial3.2,
                                                   watermazedata$Duration.Spatial3.3,
                                                   watermazedata$Duration.Spatial3.4,
                                                   watermazedata$Duration.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial <- rowMeans(cbind (watermazedata$Duration.Spatial1,
                                                  watermazedata$Duration.Spatial2,
                                                  watermazedata$Duration.Spatial3),
                                           na.rm = TRUE)


watermazedata$Distance.Cued <- rowMeans(cbind (watermazedata$Distance.Cued.1,
                                               watermazedata$Distance.Cued.2,
                                               watermazedata$Distance.Cued.3,
                                               watermazedata$Distance.Cued.4,
                                               watermazedata$Distance.Cued.5),
                                        na.rm = TRUE)
watermazedata$Distance.Spatial1 <- rowMeans(cbind (watermazedata$Distance.Spatial1.1,
                                                   watermazedata$Distance.Spatial1.2,
                                                   watermazedata$Distance.Spatial1.3,
                                                   watermazedata$Distance.Spatial1.4,
                                                   watermazedata$Distance.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial2 <- rowMeans(cbind (watermazedata$Distance.Spatial2.1,
                                                   watermazedata$Distance.Spatial2.2,
                                                   watermazedata$Distance.Spatial2.3,
                                                   watermazedata$Distance.Spatial2.4,
                                                   watermazedata$Distance.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial3 <- rowMeans(cbind (watermazedata$Distance.Spatial3.1,
                                                   watermazedata$Distance.Spatial3.2,
                                                   watermazedata$Distance.Spatial3.3,
                                                   watermazedata$Distance.Spatial3.4,
                                                   watermazedata$Distance.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial <- rowMeans(cbind (watermazedata$Distance.Spatial1,
                                                  watermazedata$Distance.Spatial2,
                                                  watermazedata$Distance.Spatial3),
                                           na.rm = TRUE)

# Make a Speed variable (Distance/Duration)
watermazedata$Speed <- watermazedata$Distance.Spatial / watermazedata$Duration.Spatial

# Make working memory variables
watermazedata$Working.Duration.Trial1.1 <- watermazedata$Duration.Spatial1.1
watermazedata$Working.Duration.Trial2.1 <- watermazedata$Duration.Spatial1.2
watermazedata$Working.Duration.Diff.1 <- watermazedata$Duration.Spatial1.1 - watermazedata$Duration.Spatial1.2

watermazedata$Working.Duration.Trial1.2 <- watermazedata$Duration.Spatial2.1
watermazedata$Working.Duration.Trial2.2 <- watermazedata$Duration.Spatial2.2
watermazedata$Working.Duration.Diff.2 <- watermazedata$Duration.Spatial2.1 - watermazedata$Duration.Spatial2.2

watermazedata$Working.Duration.Trial1.3 <- watermazedata$Duration.Spatial3.1
watermazedata$Working.Duration.Trial2.3 <- watermazedata$Duration.Spatial3.2
watermazedata$Working.Duration.Diff.3 <- watermazedata$Duration.Spatial3.1 - watermazedata$Duration.Spatial3.2

watermazedata$Working.Duration.Trial1.Ave <- (watermazedata$Duration.Spatial1.1 + watermazedata$Duration.Spatial2.1 + watermazedata$Duration.Spatial3.1) / 3
watermazedata$Working.Duration.Trial2.Ave <- (watermazedata$Duration.Spatial1.2 + watermazedata$Duration.Spatial2.2 + watermazedata$Duration.Spatial3.2) / 3
watermazedata$Working.Duration.Diff.Ave <- (watermazedata$Working.Duration.Diff.1 + watermazedata$Working.Duration.Diff.2 + watermazedata$Working.Duration.Diff.3) / 3


watermazedata$Working.Distance.Trial1.1 <- watermazedata$Distance.Spatial1.1
watermazedata$Working.Distance.Trial2.1 <- watermazedata$Distance.Spatial1.2
watermazedata$Working.Distance.Diff.1 <- watermazedata$Distance.Spatial1.1 - watermazedata$Distance.Spatial1.2

watermazedata$Working.Distance.Trial1.2 <- watermazedata$Distance.Spatial2.1
watermazedata$Working.Distance.Trial2.2 <- watermazedata$Distance.Spatial2.2
watermazedata$Working.Distance.Diff.2 <- watermazedata$Distance.Spatial2.1 - watermazedata$Distance.Spatial2.2

watermazedata$Working.Distance.Trial1.3 <- watermazedata$Distance.Spatial3.1
watermazedata$Working.Distance.Trial2.3 <- watermazedata$Distance.Spatial3.2
watermazedata$Working.Distance.Diff.3 <- watermazedata$Distance.Spatial3.1 - watermazedata$Distance.Spatial3.2

watermazedata$Working.Distance.Trial1.Ave <- (watermazedata$Distance.Spatial1.1 + watermazedata$Distance.Spatial2.1 + watermazedata$Distance.Spatial3.1) / 3
watermazedata$Working.Distance.Trial2.Ave <- (watermazedata$Distance.Spatial1.2 + watermazedata$Distance.Spatial2.2 + watermazedata$Distance.Spatial3.2) / 3
watermazedata$Working.Distance.Diff.Ave <- (watermazedata$Working.Distance.Diff.1 + watermazedata$Working.Distance.Diff.2 + watermazedata$Working.Distance.Diff.3) / 3

```

Create a "subset" dataframe for each group to ease making histograms/normal curves and QQ plots by group.

```{r}
Ac_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Ac")
Fx_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Fx")
Sh_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Sh")
```

How many subjects are missing data from a specific column? (na = 'missing'). Make a variable that returns 1 (TRUE) if data is missing:
watermazedata$Duration.Spatial1.Missing <- is.na(watermazedata$Duration.Spatial1)

How many subjects are missing from Duration.Spatial1 data?
sum(watermazedata$Duration.Spatial1.Missing)

1 = missing, 0 = there, so mean will tell us proportion of cases missing data in that variable

....or simply calculate this WITHOUT making a new variable:

```{r}
sum(is.na(watermazedata$Duration.Cued)); mean(is.na(watermazedata$Duration.Cued))
sum(is.na(watermazedata$Duration.Spatial1)); mean(is.na(watermazedata$Duration.Spatial1))
sum(is.na(watermazedata$Duration.Spatial2)); mean(is.na(watermazedata$Duration.Spatial2))
sum(is.na(watermazedata$Duration.Spatial3)); mean(is.na(watermazedata$Duration.Spatial3))
sum(is.na(watermazedata$Duration.Spatial)); mean(is.na(watermazedata$Duration.Spatial))

sum(is.na(watermazedata$Distance.Cued)); mean(is.na(watermazedata$Distance.Cued))
sum(is.na(watermazedata$Distance.Spatial1)); mean(is.na(watermazedata$Distance.Spatial1))
sum(is.na(watermazedata$Distance.Spatial2)); mean(is.na(watermazedata$Distance.Spatial2))
sum(is.na(watermazedata$Distance.Spatial3)); mean(is.na(watermazedata$Distance.Spatial3))
sum(is.na(watermazedata$Distance.Spatial)); mean(is.na(watermazedata$Distance.Spatial))

sum(is.na(watermazedata$Speed)); mean(is.na(watermazedata$Speed))

sum(is.na(watermazedata$Probe.Entries.1)); mean(is.na(watermazedata$Probe.Entries.1))
sum(is.na(watermazedata$Probe.Entries.2)); mean(is.na(watermazedata$Probe.Entries.2))
sum(is.na(watermazedata$Probe.Entries.3)); mean(is.na(watermazedata$Probe.Entries.3))
sum(is.na(watermazedata$Probe.Entries.Ave)); mean(is.na(watermazedata$Probe.Entries.Ave))
sum(is.na(watermazedata$Probe.Percent1)); mean(is.na(watermazedata$Probe.Percent1))
sum(is.na(watermazedata$Probe.Percent2)); mean(is.na(watermazedata$Probe.Percent2))
sum(is.na(watermazedata$Probe.Percent3)); mean(is.na(watermazedata$Probe.Percent3))
sum(is.na(watermazedata$Probe.Percent.Ave)); mean(is.na(watermazedata$Probe.Percent.Ave))
sum(is.na(watermazedata$Probe2.Opposite.Percent)); mean(is.na(watermazedata$Probe2.Opposite.Percent))

sum(is.na(watermazedata$Working.Duration.Trial1.Ave)); mean(is.na(watermazedata$Working.Duration.Trial1.Ave))
sum(is.na(watermazedata$Working.Duration.Trial2.Ave)); mean(is.na(watermazedata$Working.Duration.Trial2.Ave))
sum(is.na(watermazedata$Working.Duration.Diff.Ave)); mean(is.na(watermazedata$Working.Duration.Diff.Ave))

sum(is.na(watermazedata$Working.Distance.Trial1.Ave)); mean(is.na(watermazedata$Working.Distance.Trial1.Ave))
sum(is.na(watermazedata$Working.Distance.Trial2.Ave)); mean(is.na(watermazedata$Working.Distance.Trial2.Ave))
sum(is.na(watermazedata$Working.Distance.Diff.Ave)); mean(is.na(watermazedata$Working.Distance.Diff.Ave))
```

Now, start checking the various aussumptions (normality, homogeneity of variance, etc.) for all *meaningful DVS* - e.g., Average Distance and Distance for days 1-3 are probably important, but Blocks 1-5 from each are *probably* not (?). If the idea is to compare groups, the assumptions need to be tested with each variable broken down by group.

??? is there a "Bonferroni correction" for multiple tests of Normality etc ???

Generate some descriptive stats for the variables of interest.

NOTE: For output reported using "e": e+02, simply "move" the decimal point 2 places to right. e-02 = move decimal 2 places to left...

Can use describe() (from the psych package) or stat.desc() function (from the pastecs package) to get some basic stats.

```{r}
# describe()
# Overall DVs (not broken down by group)
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = describe)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, describe)
# multiple variables at once:
# describe(cbind(watermazedata$Duration.Cued,
#                watermazedata$Duration.Spatial1,
#                watermazedata$Duration.Spatial2,
#                watermazedata$Duration.Spatial3,
#                watermazedata$Duration.Spatial,
#                watermazedata$Distance.Cued,
#                watermazedata$Distance.Spatial1,
#                watermazedata$Distance.Spatial2,
#                watermazedata$Distance.Spatial3,
#                watermazedata$Distance.Spatial,
#                watermazedata$Speed,
#                watermazedata$Probe.Entries.1,
#                watermazedata$Probe.Entries.2,
#                watermazedata$Probe.Entries.3,
#                watermazedata$Probe.Entries.Ave,
#                watermazedata$Probe.Percent1,
#                watermazedata$Probe.Percent2,
#                watermazedata$Probe.Percent3,
#                watermazedata$Probe.Percent.Ave,
#                watermazedata$Probe2.Opposite.Percent,
#                watermazedata$Working.Duration.Trial1.Ave,
#                watermazedata$Working.Duration.Trial2.Ave,
#                watermazedata$Working.Duration.Diff.Ave,
#                watermazedata$Working.Distance.Trial1.Ave,
#                watermazedata$Working.Distance.Trial2.Ave,
#                watermazedata$Working.Distance.Diff.Ave))
# or
# describe(watermazedata[,c("Duration.Spatial1",
#                          "Duration.Spatial2",
#                          "Duration.Spatial3")]); # ETC

# broken down by group
#by(cbind(Duration.Cued=watermazedata$Duration.Cued,
#         Duration.Spatial1=watermazedata$Duration.Spatial1,
#         Duration.Spatial2=watermazedata$Duration.Spatial2,
#         Duration.Spatial3=watermazedata$Duration.Spatial3,
#         Duration.Spatial=watermazedata$Duration.Spatial,
#         Distance.Cued=watermazedata$Distance.Cued,
#         Distance.Spatial1=watermazedata$Distance.Spatial1,
#         Distance.Spatial2=watermazedata$Distance.Spatial2,
#         Distance.Spatial3=watermazedata$Distance.Spatial3,
#         Distance.Spatial=watermazedata$Distance.Spatial,
#         Speed=watermazedata$Speed,
#         Probe.Entries.1=watermazedata$Probe.Entries.1,
#         Probe.Entries.2=watermazedata$Probe.Entries.2,
#         Probe.Entries.3=watermazedata$Probe.Entries.3,
#         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
#         Probe.Percent1=watermazedata$Probe.Percent1,
#         Probe.Percent2=watermazedata$Probe.Percent2,
#         Probe.Percent3=watermazedata$Probe.Percent3,
#         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
#         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
#         Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
#         Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
#         Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
#         Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
#         Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
#         Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
#   watermazedata$Treatment, describe)

# normality of overall variables
# shapiro.test(watermazedata$Duration.Cued)
# shapiro.test(watermazedata$Duration.Spatial1)
# shapiro.test(watermazedata$Duration.Spatial2)
# shapiro.test(watermazedata$Duration.Spatial3)
# shapiro.test(watermazedata$Duration.Spatial)
# shapiro.test(watermazedata$Distance.Cued)
# shapiro.test(watermazedata$Distance.Spatial1)
# shapiro.test(watermazedata$Distance.Spatial2)
# shapiro.test(watermazedata$Distance.Spatial3)
# shapiro.test(watermazedata$Distance.Spatial)
# shapiro.test(watermazedata$Speed)
# shapiro.test(watermazedata$Probe.Entries.1)
# shapiro.test(watermazedata$Probe.Entries.2)
# shapiro.test(watermazedata$Probe.Entries.3)
# shapiro.test(watermazedata$Probe.Entries.Ave)
# shapiro.test(watermazedata$Probe.Percent1)
# shapiro.test(watermazedata$Probe.Percent2)
# shapiro.test(watermazedata$Probe.Percent3)
# shapiro.test(watermazedata$Probe.Percent.Ave)
# shapiro.test(watermazedata$Probe2.Opposite.Percent)
# shapiro.test(watermazedata$Working.Duration.Trial1.Ave)
# shapiro.test(watermazedata$Working.Duration.Trial2.Ave)
# shapiro.test(watermazedata$Working.Duration.Diff.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial1.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial2.Ave)
# shapiro.test(watermazedata$Working.Distance.Diff.Ave)

# normality of variables broken down by group
# by(watermazedata$Duration.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Speed, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment, shapiro.test)
```

Using stat.desc

```{r}

# stat.desc()
# using basic = FALSE adds Shapiro-Wilks test, negating the need to run that separately as with describe()
# Overall DVs (not broken down by group)
stat.desc(cbind(Duration.Cued=watermazedata$Duration.Cued,
                Duration.Spatial1=watermazedata$Duration.Spatial1,
                Duration.Spatial2=watermazedata$Duration.Spatial2,
                Duration.Spatial3=watermazedata$Duration.Spatial3,
                Duration.Spatial=watermazedata$Duration.Spatial,
                Distance.Cued=watermazedata$Distance.Cued,
                Distance.Spatial1=watermazedata$Distance.Spatial1,
                Distance.Spatial2=watermazedata$Distance.Spatial2,
                Distance.Spatial3=watermazedata$Distance.Spatial3,
                Distance.Spatial=watermazedata$Distance.Spatial,
                Speed=watermazedata$Speed,
                Probe.Entries.1=watermazedata$Probe.Entries.1,
                Probe.Entries.2=watermazedata$Probe.Entries.2,
                Probe.Entries.3=watermazedata$Probe.Entries.3,
                Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
                Probe.Percent1=watermazedata$Probe.Percent1,
                Probe.Percent2=watermazedata$Probe.Percent2,
                Probe.Percent3=watermazedata$Probe.Percent3,
                Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
                Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
                Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
                Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
                Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
                Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
                Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
                Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
          basic = FALSE, norm = TRUE)
# or
# stat.desc(watermazedata[, c("Duration.Spatial1",
#                            "Duration.Spatial2",
#                            "Duration.Spatial3")], basic = FALSE, norm = TRUE); # ETC

# Broken down by group
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# multiple variables at once:
by(cbind(Duration.Cued=watermazedata$Duration.Cued,
         Duration.Spatial1=watermazedata$Duration.Spatial1,
         Duration.Spatial2=watermazedata$Duration.Spatial2,
         Duration.Spatial3=watermazedata$Duration.Spatial3,
         Duration.Spatial=watermazedata$Duration.Spatial,
         Distance.Cued=watermazedata$Distance.Cued,
         Distance.Spatial1=watermazedata$Distance.Spatial1,
         Distance.Spatial2=watermazedata$Distance.Spatial2,
         Distance.Spatial3=watermazedata$Distance.Spatial3,
         Distance.Spatial=watermazedata$Distance.Spatial,
         Speed=watermazedata$Speed,
         Probe.Entries.1=watermazedata$Probe.Entries.1,
         Probe.Entries.2=watermazedata$Probe.Entries.2,
         Probe.Entries.3=watermazedata$Probe.Entries.3,
         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
         Probe.Percent1=watermazedata$Probe.Percent1,
         Probe.Percent2=watermazedata$Probe.Percent2,
         Probe.Percent3=watermazedata$Probe.Percent3,
         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent),
   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# or
# by(watermazedata[, c("Duration.Spatial1",
#                     "Duration.Spatial1",
#                     "Duration.Spatial3")],; #ETC
#   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)

```

Test for homogeneity of variance among groups using the leveneTest() function from the car package (default uses median)... to use mean instead of median - for example:
leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment, center = mean)

```{r}

leveneTest(watermazedata$Duration.Cued, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial1, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial2, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial3, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Cued, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial1, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial2, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial3, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial, watermazedata$Treatment)
leveneTest(watermazedata$Speed, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.1, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.2, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.3, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent1, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent2, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent3, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment)

# *** IF 1 OF 3 IS SIGNIFICANT [NOT NORMAL], DOES THAT REQUIRE NONPARAMETRIC? ***
```

Visually check each variable's data for normality / outliers averaged across all groups. This info is probably not all that interestng until broken down by group.
- Histograms w/ overlaid normal curves
- Quantile–quantile (QQ) plots
- Boxplots
- Scatterplots
- Violin plots

```{r}
# Histograms with overlaid normal curve and Quantile–quantile plots
# Scatterplots:
# p <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
# use geom_crossbar()
# p + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
# Use geom_errorbar()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="errorbar", color="red", width=0.2) + stat_summary(fun.y=mean, geom="point", color="red")
# Use geom_pointrange()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="pointrange", color="red")

# Duration

hist.Duration.Cued <- ggplot(watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial1 <- ggplot(watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial2 <- ggplot(watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Speed, na.rm = TRUE),
                  sd = sd(watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = watermazedata$Speed)
qqplot.Speed
boxplot(watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Probe stuff

hist.Probe.Entries.1 <- ggplot(watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.2 <- ggplot(watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.3 <- ggplot(watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.Ave <- ggplot(watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent1 <- ggplot(watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent2 <- ggplot(watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent3 <- ggplot(watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent.Ave <- ggplot(watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Trial2.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial1.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial2.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Diff.Ave <- ggplot(watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

```

Now, visually check each variable data for normality / outliers broken down by group - histograms and QQ plots and box plots for each Treatment group.

```{r}
# Broken down by group (use the "subset" dataframes that were derived earlier)

# Ac
# Duration

hist.Duration.Cued <- ggplot(Ac_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Ac_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Ac_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Ac_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Ac_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Ac_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Ac_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Ac_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Ac_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Ac_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Ac_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Ac_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Ac_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Ac_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Ac_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Ac_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Ac_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Ac_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Ac_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Ac_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Ac_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Ac_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Ac_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Ac_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Ac_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Ac_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Ac_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Ac_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Ac_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Ac_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Ac_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Ac_watermazedata$Speed)
qqplot.Speed
boxplot(Ac_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Ac_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Ac_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Ac_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Ac_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Ac_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Ac_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Ac_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Ac_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Ac_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Ac_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Ac_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Ac_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Ac_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Ac_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Ac_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Ac_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Ac_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Ac_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Ac_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Ac_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Ac_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Ac_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Ac_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Ac_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Ac_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Ac_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Ac_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Ac_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Ac_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Fx
# Duration

hist.Duration.Cued <- ggplot(Fx_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Fx_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Fx_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Fx_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Fx_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Fx_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Fx_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Fx_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Fx_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Fx_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Fx_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Fx_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Fx_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Fx_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Fx_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Fx_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Fx_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Fx_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Fx_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Fx_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Fx_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Fx_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Fx_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Fx_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Fx_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Fx_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Fx_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Fx_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Fx_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Fx_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Fx_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Fx_watermazedata$Speed)
qqplot.Speed
boxplot(Fx_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Fx_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Fx_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Fx_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Fx_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Fx_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Fx_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Fx_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Fx_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Fx_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Fx_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Fx_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Fx_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Fx_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Fx_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Fx_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Fx_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Fx_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Fx_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Fx_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Fx_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Fx_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Fx_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Fx_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Fx_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Fx_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Fx_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Fx_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Fx_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Fx_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Sh
# Duration

hist.Duration.Cued <- ggplot(Sh_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Sh_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Sh_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Sh_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Sh_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Sh_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Sh_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Sh_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Sh_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Sh_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Sh_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Sh_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Sh_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Sh_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Sh_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Sh_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Sh_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Sh_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Sh_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Sh_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Sh_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Sh_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Sh_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Sh_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Sh_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Sh_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Sh_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Sh_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Sh_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Sh_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Sh_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Sh_watermazedata$Speed)
qqplot.Speed
boxplot(Sh_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Sh_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Sh_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Sh_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Sh_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Sh_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Sh_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Sh_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Sh_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Sh_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Sh_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Sh_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Sh_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Sh_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Sh_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Sh_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Sh_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Sh_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Sh_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Sh_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Sh_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Sh_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Sh_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Sh_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Sh_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Sh_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Sh_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Sh_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Sh_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Sh_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

```

Now visually compare groups against each other

```{r}
# Broken down by group all on 1 graph

# Duration

ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Probe stuff

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")
```


Meeting assumptions of normality / homogeneity of variance can be tough w/ large data sets because small variations can be "significant" (you can also test homogeneity of variance w/ "variance ratio" or Hartley's Fmax). Either way, if data are not normally distributed and of equal variances, parametric tests are not valid. To correct "problems" with the data:

Outliers
- remove the case / subject (especially if it was somehow "different")
- "bring the case it into the fold" using the mean + 2 or 3 SDs - Change the score to be the mean + 2 or 3 SDs
- "bring the case into the fold" using the next highest score plus one method - Change the score to be one unit above the next highest score in the data set

For non-normally-distributed data:
- Can also use "trimmed means" (removing a specific % of cases have been removed from each end)
- Can also use "M-estimator" which empirically derives the proper % to trim
- Can also use bootstrapping to estimate "true" mean / variance
- Transform the data: log, square root, or reciprocal transformations can correct for positive skew and/or unequal variance. If data are negatively skewed, you need derive a reciprocal score (reverse the scores by subtracting each score from the highest score obtained)
-- Make new transformed DVs using newVariable <- function(oldVariable)
--- Square root: watermazedata$Duration.Spatial.Sqrt <- sqrt(watermazedata$Duration.Spatial)
--- Absolute value: watermazedata$Duration.Spatial.Abs <- abs(watermazedata$Duration.Spatial.Diff)
--- Log (natural): watermazedata$Duration.Spatial.Log <- log(watermazedata$Duration.Spatial +1)
+1 needed to avoid trying to calculate log of 0
--- Log (base 10): watermazedata$Duration.Spatial.Log10 <- log10(watermazedata$Duration.Spatial)
+1 needed for base 10???
--- Reciprocal: watermazedata$Duration.Spatial.Reciprocal <- 1/(watermazedata$Duration.Spatial +1)  +1 needed to avoid trying to divide by zero